Comparative dose measurements by spiral tomography for preimplant diagnosis: the Scanora machine versus the Cranex Tome radiography unit

Objective. The purpose of this study was to determine the dose profile of the Cranex Tome radiography unit and compare it with that of the Scanora machine.Study design. The radiation dose delivered by the Cranex Tome radiography unit during the cross-sectional mode was determined. Single tooth gaps in regions 3 (16) and 30 (46) were simulated. Dosimetry was carried out with 2 phantoms, a head and neck phantom and a full-body phantom loaded with 142 thermoluminescent dosimeters (TLD) and 280 TLD, respectively; all locations corresponded to radiosensitive organs or tissues. The recorded local mean organ doses were compared with those measured in another study evaluating the Scanora machine.Results. Generally, dose values from the Cranex Tome radiography unit reached only 50% to 60% of the values measured for the Scanora machine. The effective dose was calculated as 0.061 mSv and 0.04 mSv for tooth regions 3 (16) and 30 (46), respectively. Corresponding values for the Scanora machine were 0.117 mSv and 0.084 mSv.Conclusion. Cross-sectional imaging in the molar region of the upper and the lower jaw can be performed with the Cranex Tome unit, which delivers only approximately half of the dose that the Scanora machine delivers.

Radiation metabolomics. 3. Biomarker discovery in the urine of gamma-irradiated rats using a simplified metabolomics protocol of gas chromatography-mass spectrometry combined with random forests machine learning algorithm

Abstract Radiation metabolomics employing mass spectral technologies represents a plausible means of high-throughput minimally invasive radiation biodosimetry. A simplified metabolomics protocol is described that employs ubiquitous gas chromatography-mass spectrometry and open source software including random forests machine learning algorithm to uncover latent biomarkers of 3 Gy gamma radiation in rats. Urine was collected from six male Wistar rats and six sham-irradiated controls for 7 days, 4 prior to irradiation and 3 after irradiation. Water and food consumption, urine volume, body weight, and sodium, potassium, calcium, chloride, phosphate and urea excretion showed major effects from exposure to gamma radiation. The metabolomics protocol uncovered several urinary metabolites that were significantly up-regulated (glyoxylate, threonate, thymine, uracil, p-cresol) and down-regulated (citrate, 2-oxoglutarate, adipate, pimelate, suberate, azelaate) as a result of radiation exposure. Thymine and uracil were shown to derive largely from thymidine and 2'-deoxyuridine, which are known radiation biomarkers in the mouse. The radiation metabolomic phenotype in rats appeared to derive from oxidative stress and effects on kidney function. Gas chromatography-mass spectrometry is a promising platform on which to develop the field of radiation metabolomics further and to assist in the design of instrumentation for use in detecting biological consequences of environmental radiation release.

Influence of Abutment Design on Stiffness, Strength, and Failure of Implant-Supported Monolithic Resin Nano Ceramic (RNC) Crowns

BACKGROUND Recent technical development allows the digital manufacturing of monolithic reconstructions with high-performance materials. For implant-supported crowns, the fixation requires an abutment design onto which the reconstruction can be bonded. PURPOSE The aim of this laboratory investigation was to analyze stiffness, strength, and failure modes of implant-supported, computer-assisted design and computer-aided manufacturing (CAD/CAM)-generated resin nano ceramic (RNC) crowns bonded to three different titanium abutments. MATERIALS AND METHODS Eighteen monolithic RNC crowns were produced and loaded in a universal testing machine under quasi-static condition according to DIN ISO 14801. With regard to the type of titanium abutment, three groups were defined: (1) prefabricated cementable standard; (2) CAD/CAM-constructed individualized; and (3) novel prefabricated bonding base. Stiffness and strength were measured and analyzed statistically with Wilcoxon rank sum test. Sections of the specimens were examined microscopically. RESULTS Stiffness demonstrated high stability for all specimens loaded in the physiological loading range with means and standard deviations of 1,579 ± 120 N/mm (group A), 1,733 ± 89 N/mm (group B), and 1,704 ± 162 N/mm (group C). Mean strength of the novel prefabricated bonding base (group C) was 17% lower than of the two other groups. Plastic deformations were detectable for all implant-abutment crown connections. CONCLUSIONS Monolithic implant crowns made of RNC seem to represent a feasible and stable prosthetic construction under laboratory testing conditions with strength higher than the average occlusal force, independent of the different abutment designs used in this investigation.

Differentiating the Multipoint Expected Improvement for Optimal Batch Design

This work deals with parallel optimization of expensive objective functions which are modelled as sample realizations of Gaussian processes. The study is formalized as a Bayesian optimization problem, or continuous multi-armed bandit problem, where a batch of q > 0 arms is pulled in parallel at each iteration. Several algorithms have been developed for choosing batches by trading off exploitation and exploration. As of today, the maximum Expected Improvement (EI) and Upper Confidence Bound (UCB) selection rules appear as the most prominent approaches for batch selection. Here, we build upon recent work on the multipoint Expected Improvement criterion, for which an analytic expansion relying on Tallis’ formula was recently established. The computational burden of this selection rule being still an issue in application, we derive a closed-form expression for the gradient of the multipoint Expected Improvement, which aims at facilitating its maximization using gradient-based ascent algorithms. Substantial computational savings are shown in application. In addition, our algorithms are tested numerically and compared to state-of-the-art UCB-based batchsequential algorithms. Combining starting designs relying on UCB with gradient-based EI local optimization finally appears as a sound option for batch design in distributed Gaussian Process optimization.